|
--- |
|
license: apache-2.0 |
|
base_model: facebook/wav2vec2-base |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- wer |
|
model-index: |
|
- name: fluent-clean-wav2vec |
|
results: [] |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# fluent-clean-wav2vec |
|
|
|
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0100 |
|
- Wer: 0.2638 |
|
|
|
## Model description |
|
|
|
More information needed |
|
|
|
## Intended uses & limitations |
|
|
|
More information needed |
|
|
|
## Training and evaluation data |
|
|
|
More information needed |
|
|
|
## Training procedure |
|
|
|
### Training hyperparameters |
|
|
|
The following hyperparameters were used during training: |
|
- learning_rate: 0.0001 |
|
- train_batch_size: 8 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_steps: 1000 |
|
- num_epochs: 30 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Wer | |
|
|:-------------:|:-----:|:-----:|:---------------:|:------:| |
|
| 4.7739 | 1.26 | 500 | 2.7988 | 1.0 | |
|
| 1.4369 | 2.53 | 1000 | 0.2079 | 0.5323 | |
|
| 0.2838 | 3.79 | 1500 | 0.0565 | 0.3471 | |
|
| 0.1845 | 5.05 | 2000 | 0.0435 | 0.3209 | |
|
| 0.1383 | 6.31 | 2500 | 0.0284 | 0.3011 | |
|
| 0.1131 | 7.58 | 3000 | 0.4893 | 0.2964 | |
|
| 0.1127 | 8.84 | 3500 | 0.0340 | 0.2702 | |
|
| 0.0942 | 10.1 | 4000 | 0.0155 | 0.2732 | |
|
| 0.0779 | 11.36 | 4500 | 0.0134 | 0.2667 | |
|
| 0.0665 | 12.63 | 5000 | 0.0130 | 0.2732 | |
|
| 0.0619 | 13.89 | 5500 | 0.0163 | 0.2667 | |
|
| 0.0539 | 15.15 | 6000 | 0.0514 | 0.2650 | |
|
| 0.0456 | 16.41 | 6500 | 0.0110 | 0.2662 | |
|
| 0.0405 | 17.68 | 7000 | 0.0105 | 0.2667 | |
|
| 0.0343 | 18.94 | 7500 | 0.0297 | 0.2667 | |
|
| 0.0325 | 20.2 | 8000 | 0.0109 | 0.2656 | |
|
| 0.0241 | 21.46 | 8500 | 0.0109 | 0.2662 | |
|
| 0.0214 | 22.73 | 9000 | 0.0136 | 0.2644 | |
|
| 0.0215 | 23.99 | 9500 | 0.0101 | 0.2638 | |
|
| 0.0215 | 25.25 | 10000 | 0.0101 | 0.2667 | |
|
| 0.0226 | 26.52 | 10500 | 0.0096 | 0.2638 | |
|
| 0.012 | 27.78 | 11000 | 0.0091 | 0.2644 | |
|
| 0.0111 | 29.04 | 11500 | 0.0100 | 0.2638 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.39.3 |
|
- Pytorch 2.2.1+cu121 |
|
- Datasets 2.18.0 |
|
- Tokenizers 0.15.2 |
|
|
